Search for: All records

Attracting students to computing is crucial for advancing the development of new skills and fostering positive attitudes toward the field, especially among females and minoritized populations. One promising approach involves integrating computing with artistic activities, such as music. This study examines how learner’s prior experiences influence their participation in a virtual summer camp on coding with music. The study also examines how participation in the camp influences participants' attitudes about computing, with an eye toward gender differences. Data were collected through participant surveys (N=73) and focus groups (N=48). Findings suggest that parents’ and guardians' involvement is crucial for participation and integrating coding with artistic work holds promise for attracting students to the field. Findings can inform possible paths to engaging students in computing. 

Broadening participation in computer science has been widely stud- ied, creating many diferent techniques to attract, motivate, and engage students. A common meta-strategy is to use an outside do- main as a hook, using the concepts in that domain to teach computer science. These domains are selected to interest the student, but stu- dents often lack a strong background in these domains. Therefore, a strategy designed to increase students’ interest, motivation, and engagement could actually create more barriers for students, who now are faced with learning two new topics. To reduce this poten- tial barrier in the domain of music, this paper presents the use of automated, immediate feedback during programming activities at a summer camp that uses music to teach foundational programming concepts. The feedback guides students musically, correcting notes that are out-of-key or rhythmic phrases that are too long or short, allowing students to focus their learning on the computer science concepts. This paper compares the correctness of students that re- ceived automated feedback with students that did not, which shows the efectiveness of the feedback. Follow up focus groups with stu- dents confrmed this quantitative data, with students claiming that the feedback was not only useful but that the activities would be much more challenging without the feedback. 

Researchers and practitioners have demonstrated various benefits of introducing computational thinking (CT) through music com- position coding. While researchers have studied the impacts on participant attitudes towards CT and their learning of CT concepts, more case studies are needed on both learning CT concepts as well as CT practices, i.e., the processes of constructing music coding projects. This paper presents a case study of middle schoolers in an informal learning environment focused on integrating music composition with coding in TunePad. Specifically, we collected and analyzed logs of coding events, final code products, and surveys to explore both CT concept use and CT practices exhibited by the par- ticipants as they completed open-ended music coding activities to create their own melodies with specific music and CT requirements and recommendations 

Software engineers are crowdsourcing answers to their everyday challenges on Q&A forums (e.g., Stack Overflow) and more recently in public chat communities such as Slack, IRC, and Gitter. Many software-related chat conversations contain valuable expert knowledge that is useful for both mining to improve programming support tools and for readers who did not participate in the original chat conversations. However, most chat platforms and communities do not contain built-in quality indicators (e.g., accepted answers, vote counts). Therefore, it is difficult to identify conversations that contain useful information for mining or reading, i.e., conversations of post hoc quality. In this article, we investigate automatically detecting developer conversations of post hoc quality from public chat channels. We first describe an analysis of 400 developer conversations that indicate potential characteristics of post hoc quality, followed by a machine learning-based approach for automatically identifying conversations of post hoc quality. Our evaluation of 2,000 annotated Slack conversations in four programming communities (python, clojure, elm, and racket) indicates that our approach can achieve precision of 0.82, recall of 0.90, F-measure of 0.86, and MCC of 0.57. To our knowledge, this is the first automated technique for detecting developer conversations of post hoc quality. 

Virtual conversational assistants designed specifically for software engineers could have a huge impact on the time it takes for software engineers to get help. Research efforts are focusing on virtual assistants that support specific software development tasks such as bug repair and pair programming. In this paper, we study the use of online chat platforms as a resource towards collecting developer opinions that could potentially help in building opinion Q&A systems, as a specialized instance of virtual assistants and chatbots for software engineers. Opinion Q&A has a stronger presence in chats than in other developer communications, thus mining them can provide a valuable resource for developers in quickly getting insight about a specific development topic (e.g., What is the best Java library for parsing JSON?). We address the problem of opinion Q&A extraction by developing automatic identification of opinion-asking questions and extraction of participants’ answers from public online developer chats. We evaluate our automatic approaches on chats spanning six programming communities and two platforms. Our results show that a heuristic approach to opinion-asking questions works well (.87 precision), and a deep learning approach customized to the software domain outperforms heuristics-based, machine-learning-based and deep learning for answer extraction in community question answering. 

In this work, we examine whether repeated participation in an after-school computing program influenced student learning of computational thinking concepts, practices, and perspectives. We also examine gender differences in learning outcomes. The program was developed through a school–university partnership. Data were collected from 138 students over a 2.5-year period. Data sources included pre–post content assessments of computational concepts related to programming in addition to computational artifacts and interviews with a purposeful sample of 12 participants. Quantitative data were analyzed using statistical methods to identify gains in pre- and post-learning of computational thinking concepts and examine potential gender differences. Interview data were analyzed qualitatively. Results indicated that students made significant gains in their learning of computational thinking concepts and that gains persisted over time. Results also revealed differences in learning of computational thinking concepts among boys and girls both at the beginning and end of the program. Finally, results from student interviews provided insights into the development of computational thinking practices and perspectives over time. Results have implications for the design of after-school computing programs that help broaden participation in computing.